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Visual Place Recognition (VPR) is a fundamental yet challenging task forsmall Unmanned Aerial Vehicle (UAV). The core reasons are the extreme viewpointchanges, and limited computational power onboard a UAV which restricts theapplicability of robust but computation intensive state-of-the-art VPR methods.In this context, a viable approach is to use local image descriptors forperforming VPR as these can be computed relatively efficiently without the needof any special hardware, such as a GPU. However, the choice of a local featuredescriptor is not trivial and calls for a detailed investigation as there is atrade-off between VPR accuracy and the required computational effort. To fillthis research gap, this paper examines the performance of severalstate-of-the-art local feature descriptors, both from accuracy andcomputational perspectives, specifically for VPR application utilizing standardaerial datasets. The presented results confirm that a trade-off betweenaccuracy and computational effort is inevitable while executing VPR onresource-constrained hardware.

Visual Place Recognition for Aerial Robotics: Exploring Accuracy-Computation Trade-off for Local Image Descriptors